scholarly journals Optimal Deployment of Charging Stations for Aerial Surveillance by UAVs with the Assistance of Public Transportation Vehicles

Sensors ◽  
2021 ◽  
Vol 21 (16) ◽  
pp. 5320
Author(s):  
Hailong Huang ◽  
Andrey V. Savkin
Keyword(s):  
Travel Time ◽  
Public Transportation ◽  
Aerial Surveillance ◽  
Baseline Method ◽  
Charging Station ◽  
The Monte Carlo Method ◽  
Average Travel Time ◽  
Remote Sites ◽  
Optimal Deployment ◽  
Charging Stations

To overcome the limitation in flight time and enable unmanned aerial vehicles (UAVs) to survey remote sites of interest, this paper investigates an approach involving the collaboration with public transportation vehicles (PTVs) and the deployment of charging stations. In particular, the focus of this paper is on the deployment of charging stations. In this approach, a UAV first travels with some PTVs, and then flies through some charging stations to reach remote sites. While the travel time with PTVs can be estimated by the Monte Carlo method to accommodate various uncertainties, we propose a new coverage model to compute the travel time taken for UAVs to reach the sites. With this model, we formulate the optimal deployment problem with the goal of minimising the average travel time of UAVs from the depot to the sites, which can be regarded as a reflection of the quality of surveillance (QoS) (the shorter the better). We then propose an iterative algorithm to place the charging stations. We show that this algorithm ensures that any movement of a charging station leads to a decrease in the average travel time of UAVs. To demonstrate the effectiveness of the proposed method, we make a comparison with a baseline method. The results show that the proposed model can more accurately estimate the travel time than the most commonly used model, and the proposed algorithm can relocate the charging stations to achieve a lower flight distance than the baseline method.

scholarly journals Locating Multiple Size and Multiple Type of Charging Station for Battery Electricity Vehicles

2018 ◽  
Vol 10 (9) ◽  
pp. 3267 ◽  
Author(s):  
Shaohua Cui ◽  
Hui Zhao ◽  
Huijie Wen ◽  
Cuiping Zhang
Keyword(s):  
Travel Time ◽  
Electric Vehicles ◽  
Sensitivity Analyses ◽  
Mixed Integer ◽  
Range Limit ◽  
Mixed Integer Linear Program ◽  
Charging Station ◽  
Different Types ◽  
Initial Charge ◽  
Charging Stations

As environmental and energy issues have attracted more and more attention from the public, research on electric vehicles has become extensive and in-depth. As driving range limit is one of the key factors restricting the development of electric vehicles, the energy supply of electric vehicles mainly relies on the building of charging stations, battery swapping stations, and wireless charging lanes. Actually, the latter two kinds of infrastructure are seldom employed due to their immature technology, relatively large construction costs, and difficulty in standardization. Currently, charging stations are widely used since, in the real world, there are different types of charging station with various levels which could be suitable for the needs of network users. In the past, the study of the location charging stations for battery electric vehicles did not take the different sizes and different types into consideration. In fact, it is of great significance to set charging stations with multiple sizes and multiple types to meet the needs of network users. In the paper, we define the model as a location problem in a capacitated network with an agent technique using multiple sizes and multiple types and formulate the model as a 0–1 mixed integer linear program (MILP) to minimize the total trip travel time of all agents. Finally, we demonstrate the model through numerical examples on two networks and make sensitivity analyses on total budget, initial quantity, and the anxious range of agents accordingly. The results show that as the initial charge increases or the budget increases, travel time for all agents can be reduced; a reduction in range anxiety can increase travel time for all agents.

Online Charging and Routing of Electric Vehicles in Stochastic Time-Varying Networks

2017 ◽  
Vol 2667 (1) ◽  
pp. 61-70 ◽  
Author(s):  
Ehsan Jafari ◽  
Stephen D. Boyles
Keyword(s):  
Travel Time ◽  
A Priori ◽  
Cost Savings ◽  
Time Varying ◽  
Charging Station ◽  
Current State ◽  
Time Varying Networks ◽  
Charging Stations ◽  
Generalized Cost ◽  
Stochastic Time

This paper formulates the problem of online charging and routing of a single electric vehicle in a network with stochastic and time-varying travel times. Public charging stations, with nonidentical electricity prices and charging rates, exist through the network. Upon arrival at each node, the traveler learns the travel time on all downstream arcs and the waiting time at the charging station, if one is available. The traveler aims to minimize the expected generalized cost—formulated as a weighted sum of travel time and charging cost—by considering the current state of the vehicle and availability of information in the future. The paper also discusses an offline algorithm by which all routing and charging decisions are made a priori. The numerical results demonstrate that cost savings of the online policy, compared with that for the offline algorithm, is more significant in larger networks and that the number of charging stations and vehicle efficiency rate have a significant impact on those savings.

scholarly journals An Electric Taxi Charging Station Planning Scheme Based on an Improved Destination Choice Method

Energies ◽  
2019 ◽  
Vol 12 (19) ◽  
pp. 3781 ◽  
Author(s):  
Shi ◽  
Liu ◽  
Liao ◽  
Niu ◽  
Ibrahim ◽  
...  
Keyword(s):  
Public Transportation ◽  
Environmental Crisis ◽  
Chinese Government ◽  
Operation System ◽  
Charging Station ◽  
Fast Charging ◽  
Planning Scheme ◽  
Charging Stations ◽  
Ev Charging

The environmental crisis has prompted the development of electric vehicles as a green and environmentally friendly mode of travel. Since a reasonable layout of electric vehicle (EV) charging stations is the prerequisite for developing the EV industry, obtaining an optimal and efficient EV charging station planning scheme is a key issue. Although the Chinese government has carried out a plan to build EV charging piles in residential and working places, it cannot properly fulfill the task of matching the charging needs for public transportation vehicles such as electric taxis (ETs). How to evaluate the performance of fast charging stations (FCSs) and how to help find the optimal ET charging station planning scheme are new challenges. In this paper, an improved destination selection model is proposed to simulate the ET operation system and to help find the optimal ET charging station size with statistical analysis based on the charging need prediction. A numerical case study shows that the proposed method can address ET charging behavior well and can help to statistically determine the size of each ET charging station, which should satisfy the constraints on the preset proportion of the ET charging service requests.

scholarly journals A Choice Too Far: Transit Difficulty and Early High School Transfer

2020 ◽  
pp. 0013189X2094950 ◽  
Author(s):  
Marc L. Stein ◽  
Julia Burdick-Will ◽  
Jeffrey Grigg
Keyword(s):  
High School ◽  
Travel Time ◽  
Administrative Data ◽  
Public Transportation ◽  
School System ◽  
Ninth Grade ◽  
Public School System ◽  
Baltimore City ◽  
School Transfer ◽  
The Relationship

The challenge of a long and difficult commute to school each day is likely to wear on students, leading some to change schools. We used administrative data from approximately 3,900 students in the Baltimore City Public School System in 2014–2015 to estimate the relationship between travel time on public transportation and school transfer during the ninth grade. We show that students who have relatively more difficult commutes are more likely to transfer than peers in the same school with less difficult commutes. Moreover, we found that when these students change schools, their newly enrolled school is substantially closer to home, requires fewer vehicle transfers, and is less likely to have been included among their initial set of school choices.

scholarly journals Partial Power Processing Based Converter for Electric Vehicle Fast Charging Stations

Electronics ◽  
2021 ◽  
Vol 10 (3) ◽  
pp. 260
Author(s):  
Jon Anzola ◽  
Iosu Aizpuru ◽  
Asier Arruti
Keyword(s):  
Electric Vehicle ◽  
Power Converters ◽  
Power Ratio ◽  
Charging Station ◽  
Fast Charging ◽  
Full Power ◽  
Processing Architectures ◽  
Charging Stations ◽  
Fast Charging Station ◽  
Partial Power Processing

This paper focuses on the design of a charging unit for an electric vehicle fast charging station. With this purpose, in first place, different solutions that exist for fast charging stations are described through a brief introduction. Then, partial power processing architectures are introduced and proposed as attractive strategies to improve the performance of this type of applications. Furthermore, through a series of simulations, it is observed that partial power processing based converters obtain reduced processed power ratio and efficiency results compared to conventional full power converters. So, with the aim of verifying the conclusions obtained through the simulations, two downscaled prototypes are assembled and tested. Finally, it is concluded that, in case galvanic isolation is not required for the charging unit converter, partial power converters are smaller and more efficient alternatives than conventional full power converters.

scholarly journals Energy Management System Optimization of Drug Store Electric Vehicles Charging Station Operation

2021 ◽  
Vol 13 (11) ◽  
pp. 6163
Author(s):  
Yongyi Huang ◽  
Atsushi Yona ◽  
Hiroshi Takahashi ◽  
Ashraf Mohamed Hemeida ◽  
Paras Mandal ◽  
...  
Keyword(s):  
Power Generation ◽  
Electric Vehicle ◽  
Electric Vehicles ◽  
System Optimization ◽  
Mixed Integer ◽  
Charging Station ◽  
Electric Vehicle Charging ◽  
Photovoltaic Power ◽  
Vehicle Charging ◽  
Charging Stations

Electric vehicle charging station have become an urgent need in many communities around the world, due to the increase of using electric vehicles over conventional vehicles. In addition, establishment of charging stations, and the grid impact of household photovoltaic power generation would reduce the feed-in tariff. These two factors are considered to propose setting up charging stations at convenience stores, which would enable the electric energy to be shared between locations. Charging stations could collect excess photovoltaic energy from homes and market it to electric vehicles. This article examines vehicle travel time, basic household energy demand, and the electricity consumption status of Okinawa city as a whole to model the operation of an electric vehicle charging station for a year. The entire program is optimized using MATLAB mixed integer linear programming (MILP) toolbox. The findings demonstrate that a profit could be achieved under the principle of ensuring the charging station’s stable service. Household photovoltaic power generation and electric vehicles are highly dependent on energy sharing between regions. The convenience store charging station service strategy suggested gives a solution to the future issues.

Estimating Express Train Preference of Urban Railway Passengers Based on Extreme Gradient Boosting (XGBoost) using Smart Card Data

2021 ◽  
pp. 036119812110133
Author(s):  
Eun Hak Lee ◽  
Kyoungtae Kim ◽  
Seung-Young Kho ◽  
Dong-Kyu Kim ◽  
Shin-Hyung Cho
Keyword(s):  
Travel Time ◽  
Smart Card ◽  
Public Transportation ◽  
Gradient Boosting ◽  
Log Data ◽  
Smart Card Data ◽  
Extreme Gradient Boosting ◽  
Express Train ◽  
Total Travel Time ◽  
The Individual

As the share of public transport increases, the express strategy of the urban railway is regarded as one of the solutions that allow the public transportation system to operate efficiently. It is crucial to express the urban railway’s express strategy to balance a passenger load between the two types of trains, that is, local and express trains. This research aims to estimate passengers’ preference between local and express trains based on a machine learning technique. Extreme gradient boosting (XGBoost) is trained to model express train preference using smart card and train log data. The passengers are categorized into four types according to their preference for the local and express trains. The smart card data and train log data of Metro Line 9 in Seoul are combined to generate the individual trip chain alternatives for each passenger. With the dataset, the train preference is estimated by XGBoost, and Shapley additive explanations (SHAP) is used to interpret and analyze the importance of individual features. The overall F1 score of the model is estimated to be 0.982. The results of feature analysis show that the total travel time of the local train feature is found to substantially affect the probability of express train preference with a 1.871 SHAP value. As a result, the probability of the express train preference increases with longer total travel time, shorter in-vehicle time, shorter waiting time, and few transfers on the passenger’s route. The model shows notable performance in accuracy and provided an understanding of the estimation results.

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